{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpath = '.\\\\data\\\\'\n",
    "data = pd.read_csv(dpath + 'diabetes.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "单变量分析\n",
    "--------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e5a92b3b38>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5a92ad2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 类别比重\n",
    "sns.countplot(data['Outcome'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 根据表类别0，大致是类别1的两倍，我们可以把类别0分成两组，进行集成学习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e5a9380b00>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5a2d09a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 怀孕次数\n",
    "sns.countplot(data['Pregnancies'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 怀孕次数大体正常，不需要特别的处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e5a96730b8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5a9687278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['Glucose'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 血糖浓度有少数0点，应该是异常值，可以做特别处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e5a93f69e8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5a9738b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['BloodPressure'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 血压有少数0点，应该是异常值，可以做特别处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e5a97418d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5a97e4780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['SkinThickness'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e5a97e4dd8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5a980d0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['Insulin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e5a9884320>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5a9910898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['BMI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e5a9958eb8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5a992e048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['DiabetesPedigreeFunction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e5a98848d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5a9a54860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "变量联合分析\n",
    "---------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e5a9b88a90>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5a99b89b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#相关性分析\n",
    "data_corr = data.corr()\n",
    "sns.heatmap(data_corr, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies_Age                           0.544341\n",
       "Glucose_Outcome                           0.466581\n",
       "SkinThickness_Insulin                     0.436783\n",
       "SkinThickness_BMI                         0.392573\n",
       "Glucose_Insulin                           0.331357\n",
       "BMI_Outcome                               0.292695\n",
       "BloodPressure_BMI                         0.281805\n",
       "Glucose_Age                               0.263514\n",
       "BloodPressure_Age                         0.239528\n",
       "Age_Outcome                               0.238356\n",
       "Pregnancies_Outcome                       0.221898\n",
       "Glucose_BMI                               0.221071\n",
       "BloodPressure_SkinThickness               0.207371\n",
       "Insulin_BMI                               0.197859\n",
       "Insulin_DiabetesPedigreeFunction          0.185071\n",
       "SkinThickness_DiabetesPedigreeFunction    0.183928\n",
       "DiabetesPedigreeFunction_Outcome          0.173844\n",
       "Glucose_BloodPressure                     0.152590\n",
       "Pregnancies_BloodPressure                 0.141282\n",
       "BMI_DiabetesPedigreeFunction              0.140647\n",
       "Glucose_DiabetesPedigreeFunction          0.137337\n",
       "Insulin_Outcome                           0.130548\n",
       "Pregnancies_Glucose                       0.129459\n",
       "SkinThickness_Age                         0.113970\n",
       "BloodPressure_Insulin                     0.088933\n",
       "Pregnancies_SkinThickness                 0.081672\n",
       "SkinThickness_Outcome                     0.074752\n",
       "Pregnancies_Insulin                       0.073535\n",
       "BloodPressure_Outcome                     0.065068\n",
       "Glucose_SkinThickness                     0.057328\n",
       "Insulin_Age                               0.042163\n",
       "BloodPressure_DiabetesPedigreeFunction    0.041265\n",
       "BMI_Age                                   0.036242\n",
       "DiabetesPedigreeFunction_Age              0.033561\n",
       "Pregnancies_DiabetesPedigreeFunction      0.033523\n",
       "Pregnancies_BMI                           0.017683\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#通过排序查找最相关参数\n",
    "def corr_to_series(corr_metrix):\n",
    "    tmp_data_list = []\n",
    "    tmp_feature_list = []\n",
    "    start_pos = 1\n",
    "    for index, row in corr_metrix.iterrows():\n",
    "        for i in range(start_pos, row.size):\n",
    "            tmp_data_list.append(row.iloc[i])\n",
    "            tmp_feature_list.append(index + '_' + row.index[i])\n",
    "        start_pos = start_pos + 1\n",
    "    return pd.Series(data=tmp_data_list, index = tmp_feature_list)\n",
    "data_corr_sorted = abs(corr_to_series(data_corr)).sort_values(ascending = False)\n",
    "data_corr_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5a9af79b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5a9da63c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5a9c423c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5a9cceac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5aa010b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PIiIi02G4ERGR6TDc9LHA6AK6wNq8w9q8w9q8E8y1hSRecyMiItNhy42IiEyH4UZERKbDcCMiItNhuBERkemE1ZTtp806Qy18/2OjyyAi8lpanFXz9Fux/cep3z/3Gn46a7A/Swo0Td8/rFpuFRUVRpdARBQwjsYahOsDFcIq3IiIKDww3IiITKzV7jC6BEMw3IiITMoigsaW8Ay3sBpQQuZkt7XiaGkJbC3N4Iw7ZAYigoioaKRkZsEa4f1T7UWAJptdx8pCB8ONQt7R0hL0SEpCamoqJFyvnpOpKKVQWVmBo6UlSM/q5/V+BEBTS3iGG7slKeTZWpoZbGQqIoLU1DTYWpp92o9FBPUtNp2qCi0MNwp5SikGG5mOiPjczW6xCCrqW3SqKLQw3IiITCrCIiit8a31F6oYbkTkkSWLF2P4sCEYkj8If/rjHzpd569//T+MLBiOMWNGYfas07Fv375jn91/370YPaoAo0cVYOHCNwJV9nG++fprTJgwDjHRkXj77bc6XaehoQFzzz0HI4YPxaiRI/DL++/z+DhPPfUkhuQPQmSEBeXl5cd9tmzpUowbNwajRo7AqTNnePM1umW1CMpqw3OgFcONKEjZbP65VmK3ez/AwG634847b8cHH36MjZsK8fobr2PLli0nrDdm9Bh89/1qrFu3Aef/6Ee4/757AQAff/QR1q1bhzVr12HFt9/hL3/5M2pqaryux1t9+vbFCy+8hEsuvazL9e6++x5sLtyK1Wt+wLfffovFn3zi0XGmTJmKxUs+Q79+xw8Kqaqqwh133IZ3330fGzZuxutvLPT4O2hhtQgaW+2oaw6/624MNyIf7d27FyOGD8U111yNMWNG4eKLLkRDQwMAYO3atTh15gxMnDgeZ505B4cOHQIAPP/8PzF58kSMHTsaF114wbH1r732Gvzsnrtx+mmn4v777sXXy5Zh3LgxGDduDMaPH4va2loopXDvL37e1voZPfJY62fZ0qU47dSZuPiiCzFi+FBceeUVx35jHzggD7999BGcMn0a3nrrTa+/66pVqzBgwED0798fUVFRuPiii/HBovdPWG/GzJmIi4sDAEyaNBnFxcUAgK1bt2D69OmIiIhAfHw8Ro4ciSVLFgMAHvrNg/jgg0Un7OuRhx/CVVf9GLNOPw1DhwzG88//0+v62+Xm5mLkyJGwWNyfAuPi4jBj5kwAQFRUFMaMHYPig23fo6ysDBddeAEmT56IyZMnYsWKFZ3uY8yYMcjNzT1h+WuvvYr5889D3759AQCZmZk+fqPOWS1t16KP1DT5Zf/BjLcCEOmgqKgIzy14HlOnTsX111+LZ595Gnfc+RPc9ZM78c677yEjIwMLF76BBx74FZ5//kWcd975uP76GwAADz7wa7z44gu4/fY7AAA7duzAkk8/g9Vqxfx5c/H3vz+JqVOnoq6uDjExMXj33XewYcMGrP1hPcrLy3HS5ImYNm06AGD9+nXYsHEzsrKyMH3ayVixYgVOPvlkAEBMTAyWff3NCbW/+up/8Ze//PmE5QMHDMQbC48PwpKSg8jpk3PsfXZODlat+r7Lv5uXXnoBc+bMAQCMHDkKjz76CO766d1oaGjAsqVLMWzoMADAQw8/4nYfmzZtxIoVK1FfX48J48firLPORlZW1nHrzDhlOmrrak/Y9k9/fBynnX56lzV2p6qqCh99+CHuuOMnAIC7f3oX7vzJXTj55JOxf/9+nH3WHGzafGIL1p0dO7ajtbUVp506E7V1tbjjjjtx5ZU/9qnGzkRZLXAA2Flaj4GZibrvP5gx3Ih00KdPH0ydOhUAcPllV+DJJ/+B2WfMQWHhZsyZMxtAW5de7169AQCFmzfjwQcfQFV1Ferr6jBr9uxj+/rRBRfAarUCAKZMmYKf/+weXHrZZTjvvPORk5ODFcuX4+JLLoHVakXPnj0xbfopWLNmNZISkzBhwkTk5LSFz6jRo7Bv395j4XbhRRd3Wvtll12Oyy67XNP37OzaTVcjVf/73/9g7Zq1+PKrpQCAWbNnY82a1Zg2bSoy0jMwafJJsEZ0fxqae+5cxMbGIjY2FqfMmInVq1dh3rz5x62zdNnXmr6Dp2w2G664/DLcdvsd6N+/PwDgiy8+x5at/wuzmpoa1NbWIjFRW4DYbDb8sPYHfPrZ52hsbMS0k6dg0qTJGDxY39n7I60WNAPYWVoLoJeu+w52DDciHXQ8wbcP4x42bDiWr/j2hPWvu+4avPX2uxg1ahReeeVlLFu27Nhn8fHxx37+xb334cyzzsbiTz7GyVNPwuIln0HB/eCA6OjoYz9brdbjrtu57teVJy237OwcFB8oPvb+YHExsnpnddwUAPDF55/jD4/9Hl98ufS4uu7/5a9w/y9/BQC48orLMWjgILffp11nf78d+avldvPNN2LgoIH4yU/uOrbM4XBg+fJvERsbe9y6Z505B0dKj2DcuHFYsOB5t/vMyc5Belo64uPjER8fj5OnTcPGjRt0DzeLABmJ0dh+pE7X/YYCXnMj0sH+/fuxcuVKAMDrb7yGqVOnIj8/H+XlZceWt7a2orCwEABQW1uL3r17o7W1Fa+9+qrb/e7atQsFBQX4+S/uxbhx41FUtA3Tpk3HmwsXwm63o6ysDMu/+RoTJkz0uvbLLrsca9euO+HVMdgAYMKECdi5cwf27NmDlpYWvLHwDZxz7twT1lu3bh1uvfVmvPPu+8ddT7Lb7ccePbVx40Zs2rTxWKv1V7+8H++9926nNS76YBGamppQUVGBr5ctxfjxE05YZ+myrzv9Hr4E24MP/Bo11TX4v/974rjls2bNxtNPPXns/fr16wEAH3+yGGvXrusy2ADg3LnzsHz5cthsNjQ0NGD1qlUYMmSo13V2JbtHLHaUnhj6ZsdwI9LB0KFD8e9/v4IxY0bhaOVR3HTzLYiKisLrb7yJX95/H8aOHY3x48Zg5cq2VtxDDz+CqVMmY84Zs5E/JN/tfv/+9ycwelQBxo4djdjYWMyZcybmzz8PBQUFGDd2NGbNOg2P/eGP6NUrMF1OERER+Nvf/oGzz5qDghHDcOEFF2L48OFt38llQMh99/4CdXV1uOSSizBu3BicN38egLaAnzljOkYWDMctN9+El1/5NyKc3ZKbN29Gz56df48JEyZg7rnn4OSpJ+GXv/r1CdfbPLV69Wrk9uuDt996E7fecjNGjRxx7LNx48YAAIqLi/HYY7/H1q1bMGHCOIwbNwYvvNAWWn994m9Yu3YtxowZhZEFw7FgwbOdHucf//g7cvv1QXFxMcaOGYUbb7weQNv/L2eccQbGjhmFKSdNwjXXXocRI0Z0ug9f5aTEYmdpHZpaw2saLgmn+x9Gjx2vvlje9cVvCj2H9hT57bdeLfbu3Yv5887F+g2bDKvBDM46cw4+/mTxCcsfefghJCQk4O57fmZAVcbatm0reucd/8uPJ0/iHlowWt36tzfxl0+3440bJ2NS/zTdazQAn8RNRKGjs2Aj3w3plQQBsHJ3hdGlBBQHlBD5KDc3l602P3rwNw8ZXUJIS4iOQF56PL4Ls3Bjy41Cnh4TzBIFGz0nBB/SOwk/7K8Kq+tuDDcKeRFR0aisrGDAkWm0P88tIiq6+5U1GN47CS02B1btqdRlf6HA0G5JEXkRwDkASpVSJwwVEpHLAdzrfFsH4Bal1AbnZ3sB1AKwA7AppcYHpGgKOimZWThaWoLy8vKABZzN0f1xIix8DA95x/VJ3HoYkZ2MmEgLFhcexvTBGbrsM9gZfc3tZQBPAviXm8/3ADhFKXVURM4EsADAJJfPZyqlyjvflMKFNSLSp6cVa1FU7vtjQ/LT9fktnMhTUREWjMrpgSWbD+ORucMRYTV/p52h4aaU+lpEcrv43HVqh+8A5Lhbl0hvegSau/0x6CjQpgxIx/d7KvHNznLMzPfPRM3BJJTi+zoArs+bUAA+FZG1InKju41E5EYRWSMiayrKy/xeJIW+ovJm3YPN3TH8fRwKP67nvKOV/xshObZvDyTGROCttcVdbG0eIRFuIjITbeF2r8viqUqpsQDOBHCbiEzvbFul1AKl1Hil1Pi09PDoaybvGBU2DDnSk+s5LyX1fzdtR1gtmDogHUs2H0ZZrfn/fwv6cBORkQCeBzBPKXXs1xClVInzz1IA7wLwfnI9CnvBEC4MOfK3WcN6wuZQeG3VfqNL8bugDjcR6QvgHQBXKqW2uyyPF5HE9p8BzAaw2ZgqKZQFY6AEY01kDlk9YjEqJxn/XrnP9Pe8GRpuIvIagJUA8kWkWESuE5GbReRm5yoPAkgD8LSIrBeRNc7lPQEsF5ENAFYB+Egpxbl7yCPBHiAMOfKHs0dmoayu2fTX3oweLXlpN59fD+D6TpbvBjDKX3WR+YVSaLTXyhGWpIcRWUkYmJmAZ5buwsUT+iDSpLcFGH2fG1FA6RVqW8u638/QDH3DqKi8mQFHPhMRnD8mG39aUoQ3Vh/AFZP9e4+oURhuFDZ8DTYtgdbV+nqEHVtxpIfRfXpgSK9E/O3zHTh/bDbioswXBeZsjxJ14G2wbS1rPvbylV77AXifHPlGRHDpxL4oq2vGc8t2G12OX5gvrok68CYA9AqhrvatV7dlx+/HVh1pMbhnIk4akIZnlu7C+WOz0S8t3uiSdMWWG1EH/gy2jsfxx7FcW3Vs3VFXrpjUD1aL4MH3C033VA2GG5maJyd2f4VNMByXIUedSY2PwoXjc7BsexmWFB42uhxdMdzItDwNNl+PFegBK95gyFFHs4f1Qr+0ODy0aAvqm21Gl6MbXnMjU/JXsHW3384+9+QamN7X49zhbQXUzmoRXDs1D79ZVIjHlxThobnDjS5JF2y5ken4I9h8afF4s22gWnFEQNvgkjnDe+Hlb/dixU5zPCKT4UZhS0uA6NmN5+m+AnUtjggALpnYB1k9YnDPmxtQ3dhqdDk+Y7iRqWg9WWsNNn/wJuT8iQFHABAdYcWtMwaitKYJDy8qNLocnzHcKOx0FxaBGnTBgKNgMyAjAeeNycY76w7ik02HjC7HJxxQQqah5QStJdj0Op6WARueTKe1tazZrwNNOMiEAGD+mGysO1CF+9/dhHG5KchMjDG6JK+w5UamoEfLQ8s+PLkx2tN1tTDqXjwKHxEWC249ZSAamu247+1NIXtzN8ONwkZXoaBliL8vAapnwAH+66Zk9yQBQHZKLC6Z2AdfbivFwjUHjC7HKww3Cnm+dkd6c++aN7QEZDCMpmTAEQCcMbwXRmQl4eEPtmB/RYPR5XiM4Ubkhr8GlugdpuyqJH+wiOCmUwYAAO5euB52R2h1TzLcKKT5q9XmScDsKa0+7qWFP1qLej6eh603AoD0hGhcdVIu1uw7ipe/3Wt0OR7haEkKW94GW3cB5vp5XmZyt8d3N0LRl9GLrgHn76m8yNymDUrH93sq8PjibThtSCZy00Pj0ThsuZGp6dld50nLzJdtXOnRgvL274CtNwLaHmx63cn9YbEI7n17Ixwh0j3JcKOQ5Y/Ri+6W+xJQ3W0fiAEtvC5HvkiNj8IVk/vh+z2V+O/3+4wuRxOGG5mWpydzfwWb637c7StQIzb1+juh8DNjcAZG5iTj959sw4HK4B89yXCjsNPZCdvfwaYFg4SCmYjghmn9oZTCIx9uMbqcbjHcKCQFIgi0BFvNob2dvrzdb1ffi603Mlp6QjTOG52Nz7YcwbLtZUaX0yWGG5mSuxO4J622rnQXYt197m2L0KiAI2p3ZkFv9EqKwSMfFAb1vW8MN6JOuAsfLS2zjut7egy2lCiYRVotuGRiH+wqq8f76w8aXY5bDDeiDroKNm94u507RrTeGLjkakJuKnLT4vDE5zvQancYXU6nGG4Ucrw90erVJekNdwFn9OhJIm9YRPCjsTnYX9mATwuPGF1OpxhuZDq+XE/Su9Wm9z6IgsXYvinITIzGy9/uMbqUTjHciLrh71AysvXGgSXkLYtFMGtYT6zeexQ7jtQaXc4JGG4Utozo2gvW62/BdiwKDScPTIcA+HjTYaNLOQHDjcKCtydmLWHUXFJ07OWtQN4sTqSXHnFRyO+ViE82HzK6lBMw3IicvAmYjoHma8h1xNYSBbsxfXpg2+GUGcVWAAAgAElEQVRaVNa3GF3KcRhuRF7qKsS6+iyUuyaJOhqYmQAA2FBcZXAlxzM03ETkRREpFZHNbj4XEfm7iOwUkY0iMtbls6tEZIfzdVXgqqZgFqgBEnq2zozGQSXki7z0tnArPBhcXetGt9xeBjCni8/PBDDI+boRwDMAICKpAH4DYBKAiQB+IyIpfq2UiIhOEBtlRWJMBA7XNBldynEMDTel1NcAKrtYZR6Af6k23wHoISK9AZwB4DOlVKVS6iiAz9B1SBIdR0tXHu9LI9KmR2wkymqDqwfA6JZbd7IBHHB5X+xc5m45mVwoXV/ytOvS6Cd2B9NxKLRERljQbAuuabiCPdykk2Wqi+Un7kDkRhFZIyJrKsqD+xEN1L389GijSwhr/PsPfq7nvKOVFQE5ps2uEGUNrjgJrmpOVAygj8v7HAAlXSw/gVJqgVJqvFJqfFp6ht8KpdCi5SSd1DvXp2NEZ+V7tH5eZrLXx2LoUDvXc15KalpAjtnUakdslDUgx9Iq2MNtEYAfO0dNTgZQrZQ6BGAJgNkikuIcSDLbuYyIiAKo1e5AeV0z+qXGGV3KcSKMPLiIvAZgBoB0ESlG2wjISABQSj0L4GMAZwHYCaABwDXOzypF5FEAq527ekQp1dXAFAoTQzOiAzK0PTor3zS3AwzNYKuPvHekpgkOBeRlxBtdynEMDTel1KXdfK4A3ObmsxcBvOiPuoh85WmXpC/YJUlGKnJOmlyQ7X23uj8Ee7ckUdCKzsrvNMS6CzZPruUxuCjYFR6sQWZiNAZkJBhdynEMbbkRBZO8zOQThuMn9c7t9n43PVppvgwmITJKi82BjQerMGd4b4h0NojdOGy5UVgIlhaQryMwOwqW70Xh6Yf9R1HfbMe80VlGl3IChhuFLa3BoHcg6UWPYPNkMAmDlDpaWlSKnknRmDow3ehSTsBwI3Lhz+5BdyHp7pgMEwpmByobsKG4GpdN7AerJbi6JAGGG4Wg7k76/hja7mvrjd2RZDYfbChBbJQVPz6pn9GldIrhRmGjs0DobJm7lpS3AdXVdka22tglSd46eLQRK3aV47KJfZESH2V0OZ1iuBF5wNOA8ybYusOgIaO9tno/4qIicNvMgUaX4hbDjUzJXavE19Yb0BZYWkLO25ZeV+GlV7BxVhLyVmFJNdbuO4pbZw5AapC22gDe50YhKj892u+PX+nsvjdXvlxHC6VBJMFYExnDZnfgpRV70SclFtdOzTO6nC6x5UYE9ydwf4yeNLo7kq028taHGw/hYFUjHpk/AjGRwfUUgI4YbmRannRNdkXPgOtqX8HYHclWG7XbX9mAt38oxpwRvTAzP9PocrrFcKOQpfeJt6v9+RpweZnJIRdsRO1sdgeeXroTybGR+N38EUaXownDjUzN09ZbdwHnTch1t42/g21oRrRXwcZWG7V7+4eD2FfRgMfOL0BaQmj8f8EBJRS23A1K6W6wSntYdTXYxHW97mrwF7bUSA87jtRi0YaDuHBcDmYP72V0OZox3CikaRk16c0DTLXs19euyu6CzZvg0yvQ2GojAGhqteOZZbvQKzkGD547zOhyPMJwo7DWVYj563YDLcHhSbiwhUb+8tqq/ThU3YRXb5iExJhIo8vxCK+5UcjTEgRdBUB317z0bMWEQrCx1UYAsLG4Cp9uOYJrp+ZhyoDgm/W/Oww3ChveBpyWz7ujJSQ9CVJvB4kQaVHXbMNzX+9G/4x4/GKO7w/jNQK7JckU9OhC7G4frsGj9Vhaw8ro1po3dZB5vfLtXlQ3tOLlayYE/c3a7jDcKKx0N7hEa0gGuqsS8P+1NQYbAcD3eyqwfGc57jp9EEbm9DC6HK+xW5JMQ6+QCORJnsFGwaSmsRUvrtiDEdlJQT3jvxYMNzIVPQPOnyd8T6+vEQXCy9/uRUOzHX+5cDQiraEdD6FdPZEPtISGP6b4Cpbra+3YaiOgrTty5e4K3HX6IOT3SjS6HJ/xmhuZjieDS7Tc4O3NQJKu9qG1Ln9jqFG7mqZWvLRiL4ZnJeGmUwYYXY4uGG5kSnoHnOt+23W3f2/Dg8FGgfavb/eivtmGv1w0KuS7I9sx3Ijg/RRdetcQCAw2crXlUA1W7KrAnacNwpBeSUaXoxtzRDRRJ4KxK9DIY/t7kAyFHrtD4ZVv9yK7RyxunWGO7sh2bLmRqXl6c3d7yHjaivMWb8gmI32x9Qj2VzbgmcvHhuzN2u4w3Mj0vJm9xN8hp3eoMcjIUzVNrXhzbTGmDkjDnBGh8ygbrRhuFBa8nZ5L75DTM9QYaOSLN9cUo7HFjt/MHQ4RMboc3THcKGz4Mv+kayh5GnRspVGwKa1pwldFpbh0Uh8M7hn697R1huFGYUWPCZaNGnjCUCO9vLPuIKwiuH3mIKNL8RuOlqSwE2ohwVGOpKey2mZ8s6MMl0/ui17JMUaX4zcMNwpLoRIYoVAjhZaPNx+CRQQ3TOtvdCl+ZWi4icgcESkSkZ0icl8nn/9VRNY7X9tFpMrlM7vLZ4sCWzmZRbCGR6iEL4WWhhYblhaV4txRWcjqEWt0OX5l2DU3EbECeArALADFAFaLyCKl1Jb2dZRSP3VZ/w4AY1x20aiUGh2oesm82kPE12txemCgkT99s6McTa0OXDs1z+hS/M7IlttEADuVUruVUi0AXgcwr4v1LwXwWkAqo7BkZLCwpUb+ppTCF9uOoCA7GQU5yUaX43dGhls2gAMu74udy04gIv0A5AH40mVxjIisEZHvRGS+u4OIyI3O9dZUlJfpUTeZWCBDpv1YDDXSk+s572hlxbHle8rrcaCyEZdO7GtgdYFj5K0And01qNysewmAt5RSdpdlfZVSJSLSH8CXIrJJKbXrhB0qtQDAAgAYPXa8u/0THUePx9xo2TeR3lzPeUMLRh87563YVYEIi+Dsgt6G1RZIRoZbMYA+Lu9zAJS4WfcSALe5LlBKlTj/3C0iS9F2Pe6EcCPyla9BxzAjoyml8P3uCszIz0ByXKTR5QSEkeG2GsAgEckDcBBtAXZZx5VEJB9ACoCVLstSADQopZpFJB3AVAB/CkjVFNYYVBSKDhxtREV9C2YPM98cku4YFm5KKZuI3A5gCQArgBeVUoUi8giANUqp9uH9lwJ4XSnl2qU4FMBzIuJA23XDP7iOsiQiov/ZcKDtLqrpgzMMriRwDJ1+Syn1MYCPOyx7sMP7hzrZ7lsABX4tjojIJDYerMLgngmmnpGkI85QQkRkYk2tdmw7VIsZ+ZlGlxJQDDciIhPbfqQWNofCtEHpRpcSUAw3IiIT211eDwAYmdPD4EoCi+FGRGRie8vr0Tc1Dsmx4XELQDuGGxGRie2tqMeI7CSjywg4hhsRkUnZlcKRmmYMzzL/XJIdMdyIiEyqxeYAAAzrzZYbERGZhM3eNvdFv7Q4gysJPIYbEZFJ2RwKAiA7xdwPJu0Mw42IyKRa7Q5kJkUjOsJqdCkBx3AjIjIpm0OhT0r4dUkCDDciItOy2R1h2SUJMNyIiEzLoYC0+PB8TBPDjYjIpBxKIS0hyugyDMFwIyIysZQ4hhsREZlMajzDjYiITKZHXHhNmNxOU7iJSJyIPCAi/3S+HyQi5/i3NCIi8lVcVPjd4wZob7m9BKAZwEnO98UAfuuXioiISDcMt64NUEr9CUArACilGgGI36oiIiJdxEZFGF2CIbSGW4uIxAJQACAiA9DWkiMioiAWFxmeLTetkf4bAIsB9BGR/wKYCuBqfxVFRET6iA3TbklN4aaU+kxEfgAwGW3dkT9RSpX7tTIiIvJZdER4Dor35FtnA7ACiAIwXUTO909JRESkB4sIRMJzeISmlpuIvAhgJIBCAA7nYgXgHT/VRUREPgrPWGuj9ZrbZKXUML9WQkRE+grjdNPaLblSRBhuREQhJIyzTXPL7RW0BdxhtN0CIACUUmqk3yojIiLyktZwexHAlQA24X/X3IiIKIiF62ASQHu47VdKLfJrJURERDrRGm7bRORVAB/AZWYSpRRHSxIRBanwbbdpD7dYtIXabJdlvBWAiCiIhXGvpOYZSq7xdyFERER60fo8txwReVdESkXkiIi8LSI5/i6OiIi8J2HcMenJ89wWAchC2zRcHziX+URE5ohIkYjsFJH7Ovn8ahEpE5H1ztf1Lp9dJSI7nK+rfK2FiMhsVNuDXMKS1mtuGUop1zB7WUTu8uXAImIF8BSAWWh7+OlqEVmklNrSYdU3lFK3d9g2FW1PKhiPtmt/a53bHvWlJiIiMgetLbdyEblCRKzO1xUAKnw89kQAO5VSu5VSLQBeBzBP47ZnAPhMKVXpDLTPAMzxsR4iIjIJreF2LYCLABwGcAjABc5lvsgGcMDlfbFzWUc/EpGNIvKWiPTxcFuIyI0iskZE1lSUl/lYMhFRcHM959lsdqPLMYymcFNK7VdKzVVKZSilMpVS85VS+3w8dmdXOjt2EH8AINc5zdfnaJsGTOu2bQuVWqCUGq+UGp+WnuF1sUREocD1nGeNCM8HlQLaR0u+IiI9XN6nOB+D44tiAH1c3ucAKHFdQSlVoZRqv2n8nwDGad2WiCjshe94Es3dkiOVUlXtb5zXucb4eOzVAAaJSJ6IRAG4BG0jMo8Rkd4ub+cC2Or8eQmA2c6QTUHbzeVLfKyHiIhMQutoSYuIpLSPRnSOVtS6baeUUjYRuR1toWQF8KJSqlBEHgGwxjmX5Z0iMheADUAlgKud21aKyKNoC0gAeEQpVelLPUREZhPGDTeIUt1/fRH5MYD7AbyFtr+viwD8Xin1L/+Wp6/RY8erL5Z/b3QZREReS4uzar4zOyEnX9UVF/mzHCNo+v5ap9/6l4isAXCqc8fnd3I/GhERBREtjRez0hRuIvJvpdSVALZ0soyIiIJQ+Eab9gElw13fOGcXGedmXSIiCgJh3HDrOtxE5H4RqQUwUkRqRKTW+b4UwPsBqZCIiLziUCpsuya7DDel1GNKqUQAjyulkpRSic5XmlLq/gDVSEREXrI5wjPctA7n/0REpndcqJT6Wud6iIhIR802ByKtWq9AmYfWcPu5y88xaJv0eC3aRk8SEVGQam61IyHap9uSQ5LWWwHOdX3vnMD4T36piIiIdNPYGp6TJ3vbVi0GMELPQoiISH81jTajSzCE1vvc/oH/3TJhQdu8khv8VRQREemjurHV6BIMobUjdgva5n9UAKoBvKaUWuG3qoiISBfVjS1Gl2CILsNNRCIA/B5tDybdj7apt/oAeFFEVimlwvNXAiKiEBGuLbfurrk9DiAVQJ5SaqxSagyA/gB6APizv4sjIiLfVNSHZ8utu3A7B8ANSqna9gVKqRoAtwA4y5+FERGRbywiOFzdZHQZhugu3JTqZO4WpZQd4T0nJxFR0IuwCEqqGo0uwxDdhdsW57PcjiMiVwDY5p+SiIhIDxFWwcEwDbfuRkveBuAdEbkWbTOSKAATAMQCOM/PtRERkQ8iLBaUVIVnt2SX4aaUOghgkoicirbH3giAT5RSXwSiOCIi8l6EVVDd2Iqqhhb0iIsyupyA0jr91pcAvvRzLUREpKMo54TJW0pqMGVgusHVBFb4TRVNRBQmoiPaTvGFJTUGVxJ4DDciIpOyWgRp8VHYcojhRkREJtIvLQ6bD1YbXUbAMdyIiEwsLz0Bu8rqwm4aLoYbEZGJjchOgkMBK3dVGF1KQDHciIhMbGBmAmIjLfhmR5nRpQQUw42IyMQiLBYU5PTAp4VH4HCEz6yJDDciIpOblJeKsrpmrNl31OhSAobhRkRkcmP6pCA6woJ3fig2upSAYbgREZlcbJQVJ/VPw/sbSlDbFB6jJhluRERh4LShPdHYYsc7Pxw0upSA0DS3JFGwKypv1n2f+enRuu+TyCgDMuIxuGcCFny9G5dN6otIq7nbNgw3Cln+CDS998+ApGAhIpg3OhuPLynCe+sO4sLxfYwuya8YbhRS/B1oeutYL8OOjDSmTw/kpcfj/z7bjnNHZSEm0mp0SX5j7nYpmUZReXPIBVtn2r+HWb4PhRYRwRWT++FQdROe/2a30eX4laHhJiJzRKRIRHaKyH2dfH63iGwRkY0i8oWI9HP5zC4i652vRYGtnALJzCHAoKNAG9Y7CRNyU/DUV7twoLLB6HL8xrBwExErgKcAnAlgGIBLRWRYh9XWARivlBoJ4C0Af3L5rFEpNdr5mhuQoimgwu2kH27fl4zz45NyASjc+/ZGKGXOWUuMbLlNBLBTKbVbKdUC4HUA81xXUEp9pZRq/9XiOwA5Aa6RDBLOJ3mGHPlbekI0Lp3YD9/uqsB/v99vdDl+YWS4ZQM44PK+2LnMnesAfOLyPkZE1ojIdyIy391GInKjc701FeXhNXFoqOKJvQ27LMkbrue8o5XunwRw2tBMFGQn49EPt2CLCZ/UbeRoSelkWaftYxG5AsB4AKe4LO6rlCoRkf4AvhSRTUqpXSfsUKkFABYAwOix483Z/jYRX07kW8t8D4GhGcE5mrGovJkjLUkT13Pe0ILRbs95FhHcNnMg7n9nI27971p8cMfJSIyJDFid/mZkuBUDcL3RIgdASceVROR0AL8CcIpS6tjZSylV4vxzt4gsBTAGwAnhRqHDm2DTI9D03p+/ArL974chR3pJjo3EnacOwqMfbcFPXluHf141AVZLZ+2O0GNkt+RqAINEJE9EogBcAuC4UY8iMgbAcwDmKqVKXZaniEi08+d0AFMBbAlY5aQ7T4Jta1nzsVcwcq3PH3Wym5L0NKR3Eq6ekosvi8rw6IfmOY0a1nJTStlE5HYASwBYAbyolCoUkUcArFFKLQLwOIAEAG+KCADsd46MHArgORFxoC2g/6CUMs9/lTDjabCFIte69WjZsRVHepo1rBcO1zTj5W/3IrtHLG6Y3t/oknxm6AwlSqmPAXzcYdmDLj+f7ma7bwEU+Lc6CgStwRaqodYZPYOO1+JIL5dP7IuKumb87uOtiIu24vJJ/brfKIhxhhIyTDgGW0d6dFuym5L0YLEIbp85EGP69sCv392Mt9eG9rPfOLckGULvYPPnCT4QLaP27+ltS44tONJDhNWCu04bjD9/WoSfvbkBrXYHLpnY1+iyvMJwo6DVXbAFqsXizXG8DRpfQo4BR3qIirDgZ7Pz8dfPt+O+dzahrtmG66eF3jU4dktSwGkJi66CLRRubPZ1gmRvuyuD/e+FQkNUhAX3zBqMiXmp+O1HW/HnJUUhN00Xw40CSo9gC0XeBp03IReqf0cUXCKsFtx56iDMzM/Ak1/txD0LN6DF5jC6LM3YLUlBxddg8/bEHsjuPG+G8W8ta/aoq5K3CpAerBbBDdP6Iz0hGm+uLcah6iY8e+U4JMcG/0wmDDcKmO6Cx12wdbedHi0VI0LR0wDyNODaj8GAI1+ICM4fm4OMxGgs+Ho35j+1As9fNR4DMhKMLq1LDDcKCt4EWzB0v3VVg9ZQ8STkGHBklGmDMpCREI2/frEd859agacuG4vpgzOMLsstXnOjgPAmiNxtEwoDSgDPr7NpXZcDTcgoQ3on4bfzCpAaF4WrX1qFZ5buCtqBJmy5keE8OVlrOUnvKa326Ph5mckere8N17q7a0VpaWmxBUdGyUiMxkNzh+O5r3fhj4u34Yf9R/HnC0cF3XU4hhv5XVeB5El3pLv9eBpmvmyvRxBq6YbUsg4DjowSE2nFnacOQn7Pw/jP9/tx7j+W49krxmFYVpLRpR3DcKOgozXYfA01b2g9ppYQ1BI03a3DgCOjiAjmjOiN/hkJ+NsXO3De0yvw2/kjcOH4Pt1vHAC85kZ+5WmrLZiDzRN7SquPvbqi5Tqbt6NMfT0ukRaDeybi9+cVYFBmAn7+1kbc/85GNLXajS6LLTcKbp4GW82hvV4fK6l3rtfbdqW93q5ac921pvzRgtOyXyItkmMjcf+ZQ/Hm2gN4bdUBbCyuxrNXjEOf1DjDamK4kSG0tNq0BpsvgebpfnwJwO5CrrvrbP4KIgYc6cFiEVw8oS8GZibimaU7cc4/luOJi0dj5pBMY+ox5KgUFvTu9vJnsGlVc2hvt6/udNdd6e29fWZ+NBCFjnH9UvC78wqQEheJa15ejb98WgS7I/C3CzDcKOC8abV1DAOtQQIAzSVFaC4p8qREn2gNumAKOF5/Iz31TIrBw3NH4JTBGfjHlztx3curUd3QGtAa2C1JQa+zYOtMdwGmJeCis/K1lqVJe63uujP3lFZ32U0Z6Dkv2T1JeomKsOCm6f0xICMB/1q5F+c+uRz//PF45PdKDMjx2XKjgPL2Wlu7zoJNz5ZZ+77cvbzVVUuuq27KrmZpcceX7km24EhPIoJZw3rigXOGoaapFfOeWo4PN5YE5NgMN/ILvU6S3Q2lD2R3Y/vxfAm/rroqgyXgiPTWfrtA39Q43P7qOjz2yVbY7P59fA67JclQvrTavO2G1Lvr0d0x3R2nq65Kd92UgewyZPck+UNKXBQeOHsYXlm5D88t243CgzV4+oqxSIrxz7RdDDcKWq4tGS3BprUV19V6egZf+3G6CjlPAq4zXQWRt/e+EflLhNWC607OQ//0eLywYg8ufGYlXr52Anonx+p+LHZLUsB011WmtSvTl2DTsm+9rrO57tMdT25l4PUwMouZQzJx75wh2F/ZgPlPrcDWQzW6H4PhRobp6mTdVautI39fd9NjkImnQenJ9GK8NYBCUUF2Mn5z7jC02hUufm4lNhyo0nX/DDfSnT9PjB0DItADSjrjSdh19nlXoyg7YuiQmfRLi8dD5w5DTKQVlz//PdbuO6rbvhluFBRcT9ruWm1ag6354FbNL3/oLuQ8CTg9sPVGwSwjMQYPnjMMCdFWXPnC99hYrE8LjuFGAaH30PROr7t5EVj+DDtPW5WdBZzW1huDiEJZWkI0HjhnOBKiI3Dty6txoLLB530y3MgQep+M9QomvcPOXSsu0N2pvO+Ngl1qfBR+ccYQNLbacfVLq1Dd6Nt0XQw3ClruuiRP6J70U/di+74D2bLT2nojMqPslFjcPSsfeysa8Ot3N/m0L4YbGc7d9bZg5O11O71bauyaJLMa1jsJPxqbgw82HsL76w96vR+GG4U0f7baPKlBS9Bp7Z4M9GN8usLAJCPMHZWFQZkJeOC9zahqaPFqHww3CnpddUl2tY0/JkDu9rh+Cls9WrS87kahwmoRXD+tP2qabHhx+R6v9sFwo5DlLki0hJc/Q66rgAuG+/KIQkHf1DhMzEvFiyv2evUsOIYbBSVvu+Y8DQ9/hZwnLThvj88uQzK7uaOyUNdsw5LCwx5va2i4icgcESkSkZ0icl8nn0eLyBvOz78XkVyXz+53Li8SkTMCWTe5p+WEG4wn5WBrUQXTdTcio/RPj0ePuEh8vaPM420NCzcRsQJ4CsCZAIYBuFREhnVY7ToAR5VSAwH8FcAfndsOA3AJgOEA5gB42rk/orAWjL84EHlLRFCQnYyVuyo83tbIlttEADuVUruVUi0AXgcwr8M68wC84vz5LQCniYg4l7+ulGpWSu0BsNO5PwpCgRrI4GvrK9hab0QEZCREo7K+BQ6H8mg7I8MtG8ABl/fFzmWdrqOUsgGoBpCmcVsAgIjcKCJrRGRNRbnnTVsiolDies47Wul5iyfYxERaoQA0tNo92s7IcJNOlnWMZnfraNm2baFSC5RS45VS49PSMzwskYgotLie81JS04wux2fNNgcEQISls9O+e0aGWzGAPi7vcwCUuFtHRCIAJAOo1LgtBYlAPQ3a16do6/kUbiLSx4GjDeibFoeYSM+GVRgZbqsBDBKRPBGJQtsAkUUd1lkE4CrnzxcA+FIppZzLL3GOpswDMAjAqgDVTRS08tMD84sEUSA4lMLusjoM7ZXk8baGhZvzGtrtAJYA2ApgoVKqUEQeEZG5ztVeAJAmIjsB3A3gPue2hQAWAtgCYDGA25RSnnXIkl9oObkG4wk42FptSb1zjS6ByHCFJTUor2vBmQW9PN42wg/1aKaU+hjAxx2WPejycxOAC91s+zsAv/NrgWSYpN65Xt3rFZ2V79GoR3+FWnT2UM3H87aGYPwlgUhPX2w9guTYSJwx3PNw4wwlFLI8CZDO1gl0sBGRdkWHa/H9nkpcMbmvx9fbAINbbkRauLbGtLbMjOpm9Few5WUm+7yPQA3sIfKV3aHw0oo96JUUg1tnDPRqHww3CmnR2UMNfeyNJ2GmtUsymK63seuTjPDW2mLsq2zAM5ePRXy0dzHFcCPD5adHH5s2Ki8zOWgfWOpLq0zvlmRnocMgIjNYvbcS760/iIvH98GcEZ5fa2vHa24UtFxbMK7h0DEo/HmNKzp76LGX7vvW2GpjlySFi30V9Xhm6S6MzEnGw/OGo222Re+w5UaGcG2t6UGv7km9Q8xdiy3Ybj0gMlpJVSMe+2QbkmIj8OwV47waROKK4UYBMTQj2ucJlLsbWNIeTJ6EnF9bfR4GmNZWW6C6JNnNSYFSWtOE3328FREWwas3TEZWj1if98lwo6Dg7rpbV/e7uRs5afRQ/O5CLdCDSNglScHsQGUD/rB4G+wOhTdumowBGQm67JfhRrrTu8vRVcdA8/SmbX/U4+v67oJNa6uNKFRtP1KLx5cUITbKildvmIQhXkyz5Q7DjQzTVQhqbb0B/g04va6NebofTwaRdBV43rbaGKLkb6v3VuLJr3aid3IM/nPdJPRJjdN1/ww3CpjurrtpbfF1er3NGR6+hJzfZizpYr+edEcycMgMlFL4YEMJXl99ACNzkvH8VROQkaj//9sMNwpaXbXe3F5v6xAk7kIwELwNNaNbbUT+0mJz4Pnlu/HNjnKcO7I3Hr9wlM+jIt1huJGhOrbWumq9aQ04V4Eecq/leN4EWyBbbWwhkj+U1jThiS92YE95PX56+mDcedpAn+5j6w7DjfzCXUh5ektAdzOW6NEdqZUeQalnsDg9+O4AAA1mSURBVLHVRqFi3f6jeHrpLogAz/94PE4f1tPvx2S4keF8ab2102tQib9aet52QwY62NhqIz05HApv/1CMd9YdxJBeiXjuynHolxYfkGMz3CjodWy9tQdFx5ALxlk/uhsw4k2w+QuDjfRU09SKp77ciY0Hq3HBuGz8dn6B366vdYZzS1LAdday6Hhi7fi+sxAIptnzXSX1zj326oq3wcah/xTsdpXV4VfvbsLWwzV47PwCPH6B/waOuMOWG/mNrzdzd9y+s+tv7lpx/qBXmHY3GjLQwUakF6UUPt9ain+t3IueSdF4+5YpGJnTw5BaGG5kiM4GlmgJQ3cDTHwNuUC0An0JNS2fe4utNtJDs82OF5bvwTc7ynHK4Aw8cfFopMRHGVYPw42CWmeB19UIymDsqtRy35qvweZNq42hRno5VN2IJz7fgQOVDfjp6YNxx6kDYbH4b5i/Fgw38quuWmNaW2/uAg5A0D7YVOuN2FoChsFGwWz13ko8u2wXIq0WvHTNBMzIzzS6JAAMNwpCWgMOCGzI6fHQUFcMNgplDofCG2sOYNGGEhRkJ+OZK8YiJ0Xf+SF9wXAjv/O09eZum+4mWm7nadDpHVrd0SPUAAYbGaehxYYnv9qJdfurcOnEvnho7jBERwR2NGR3GG5kOE9mLdE66CTYeBIqDDYKZkdqmvDnT4twqKoJj84bjitPyjW6pE4x3CggvLktwN027Sdpfz0zTi+ehonW9RlsZJQtJdV44osdsIjg39dNxJSB6UaX5BbDjYKCJ92Trp8BxoScnmHhyb48DTaGGunl861H8PK3e5GbFocXrpqA3PTATKPlLYYbBUx3rbeuAg5wH2IdT+DetBCN4OlxGWxkBIdS+PfKfVhceBgzBmfg75eNQVJMpNFldYvhRkGlq+tvWrs2g/2k7k19DDYyQqvdgWeW7sLK3RW4dmoefnX2UFgNvn9NK4YbBZSWgNIj4IKNt2HD62tklKZWO/7vs+3YdLAa9585BDedMsDokjzCcKOA0yPggOAeUOJrwHACZDJSY4sdf1i8FbtK6/H4BSNx4fg+RpfkMYYbBa3ubhFwPZH7K+gCHRZ8FhsZzTXY/nHZGJxV0NvokrzCcCNDaO1e1HoPXKif2H2d0T/Uvz8Fh2abHX9cvA27Suvx5GVjcGaIBhvA57mRgfx5X1eoGJoRzWCjoGBzOPC3L3Zg+5Fa/P3S0A42gC03MpgnLTgAmmcyCWZ6hjWDjfSglMLz3+zBuv1V+O38ETh7ZGgHG2BQy01EUkXkMxHZ4fwzpZN1RovIShEpFJGNInKxy2cvi8geEVnvfI0O7DcgPXl6E3MotuTa69ar9vz0aAYb6eaDDSVYtr0Md542CFdM7md0ObowquV2H4AvlFJ/EJH7nO/v7bBOA4AfK6V2iEgWgLUiskQpVeX8/OdKqbcCWDP5kadD/IO1JReI4GWokZ42HKjC66sP4OyC3vjp6YOMLkc3RoXbPAAznD+/AmApOoSbUmq7y88lIlIKIANAFciUvLmHrWOY+BJ2wd4iZKiR3srrmvHkVzuR3ysRj184EiKhcYO2FkaFW0+l1CEAUEodEpEun24nIhMBRAHY5bL4dyLyIIAvANynlAquX+HJK77epB3sAeUNhhr5g8Oh8PTSnXAoheeuHIe4KHMNwfDbNTcR+VxENnfymufhfnoD+DeAa5RSDufi+wEMATABQCpO7NJ03f5GEVkjImsqysu8/DYUSDyZt+F1NfKG6znvaGWF2/U+2nQIWw/V4uG5w9EvLbgnQfaG36JaKXW6u89E5IiI9Ha22noDKHWzXhKAjwD8Win1ncu+Dzl/bBaRlwD8rIs6FgBYAACjx45Xnn8TMkKoTrOlBwYa+cL1nDe0YHSn57wjNU14a20xzhjeExeMywlofYFi1H1uiwBc5fz5KgDvd1xBRKIAvAvgX0qpNzt81tv5pwCYD2CzX6slQ4RTy6X9u4bL9yXjKKXwysq9iLAKHp47wlTX2VwZ1cn6BwALReQ6APsBXAgAIjIewM1KqesBXARgOoA0Ebnaud3VSqn1AP4rIhkABMB6ADcHuH4KoFCYS9IbDDIywvoDVVi3vwq/PnsoeiXHGF2O3xgSbkqpCgCndbJ8DYDrnT//B8B/3Gx/ql8LpKBkhq5KBhoZyaEU3lxbjL6pcbhqSq7R5fiVuYbHkOkFYrJkvTDIKNis3luJPeX1+OvFoxBpNffsiww3Clm+PoHbk30TmcGHG0qQlx6PuaOyjS7F7xhuZBoMJCL3dhypxc6yejw6b3jIPE3bF+ZulxIREQDgsy1HkBAdgfPHmnPof0cMNyIik2tqtWP1vkqcO6o34qPDo8OO4UZEZHJr9x1FU6sD80ab/1pbO4YbEZHJrdlXiYyEaEzMTTW6lIBhuBERmZjdobCpuBozh2TAEgYDSdox3IiITGxXWR3qW+yYkd/lw1dMh+FGRGRi2w7XAgAm5oVPlyTAcCMiMrUdR2rRLy0O6QnhdR8ow42IyMT2VzagIDvZ6DICjuFGRGRSDgWU1TZjYGaC0aUEHMONiMikWu0OKAADMhhuRERkEq12BwCGGxERmUirXUEA5KXHG11KwDHciIhMyuZwIDU+CrFRVqNLCTiGGxGRSTkcQEp8lNFlGILhRkRkUnalkMpwIyIiM3E4FFLjGG5ERGQidqWQEh9pdBmGEKWU0TUEjIiUAdjnh12nAyj3w371wNq8w9q8w9q840lt5UqpOVpWFJHFWtc1m7AKN38RkTVKqfFG19EZ1uYd1uYd1uadYK4tVLFbkoiITIfhRkREpsNw08cCowvoAmvzDmvzDmvzTjDXFpJ4zY2IiEyHLTciIjIdhhsREZkOw81HIjJHRIpEZKeI3GdwLS+KSKmIbHZZlioin4nIDuefKQbV1kdEvhKRrSJSKCI/+f/27i3EqiqO4/j3h1N5KbPSwppgEsQLkaOBaUaUXUgRn4qUHnwQevEhwYgkiMKH6KEyKHopiiDUspv4YIVdHiI0r6WZXQccvExIIpSUTv8e1jq1GaZiZmD2mt3vA5uz19r74cdeh/nPWeucvUvJJ2m0pJ2S9udsj+f+ayTtyNk2SarlVg+SRknaK2lrSblyli5JX0raJ2lX7qt9THOOCZI2S/o6v+/ml5BN0rR8vVrbaUmrS8jWJC5uQyBpFPA8sAiYCSyXNLPGSK8AfX+w+TCwPSKmAttzuw7ngDURMQOYB6zK16qEfL8BCyNiFtAJ3CVpHvAk8EzO9jOwsoZsAA8AhyrtUnK13BoRnZXfaZUwpgDPAtsiYjowi3QNa88WEYfz9eoErgd+Bd4uIVujRIS3QW7AfOC9SnstsLbmTB3AgUr7MDA5708GDtd93XKWd4E7SssHjAX2ADeQ7hjR1t9YD2OedtIfuoXAVkAl5Krk6wIm9umrfUyB8cCP5C/NlZStT547gU9LzDbSN39yG5qrgCOVdnfuK8kVEXEMIL9eXnMeJHUAs4EdFJIvT/3tA3qAD4DvgVMRcS6fUtfYrgceAv7I7csKydUSwPuSdku6P/eVMKZTgJ+Al/OU7ouSxhWSrWoZsCHvl5ZtRHNxGxr10+ffVvwLSRcCbwKrI+J03XlaIqI30jRROzAXmNHfacOZSdISoCcidle7+zm1zvfcgoiYQ5qaXyXp5hqzVLUBc4AXImI28AuFTfPltdKlwBt1Z2kiF7eh6QaurrTbgaM1ZfknJyRNBsivPXUFkXQeqbC9FhFvlZYPICJOAR+T1gUnSGrLh+oY2wXAUkldwEbS1OT6AnL9JSKO5tce0rrRXMoY026gOyJ25PZmUrErIVvLImBPRJzI7ZKyjXgubkPzOTA1f3vtfNIUw5aaM/W1BViR91eQ1rqGnSQBLwGHIuLpyqHa80maJGlC3h8D3E768sFHwN11ZYuItRHRHhEdpPfWhxFxX925WiSNk3RRa5+0fnSAAsY0Io4DRyRNy123AV+VkK1iOX9PSUJZ2Ua+uhf9RvoGLAa+Ia3RPFJzlg3AMeAs6T/XlaQ1mu3At/n10pqy3USaPvsC2Je3xSXkA64D9uZsB4BHc/8UYCfwHWnq6IIax/YWYGtJuXKO/Xk72Hr/lzCmOUcnsCuP6zvAJQVlGwucBC6u9BWRrSmbb79lZmaN42lJMzNrHBc3MzNrHBc3MzNrHBc3MzNrHBc3MzNrHBc3swGQ1Jvv5L5f0h5JN+b+DkkhaV3l3ImSzkp6Lrcfk/RgXdnN/k9c3MwG5kykO7rPIt0o+4nKsR+AJZX2PaTff5nZMHNxMxu88aRHzrScAQ5Jaj365V7g9WFPZWa0/fcpZlYxJj89YDTpsSQL+xzfCCyTdBzoJd338crhjWhmLm5mA3Mm0tMDkDQfeFXStZXj24B1wAlgUw35zAxPS5oNWkR8BkwEJlX6fgd2A2tIT0Awsxr4k5vZIEmaDowi3QB3bOXQU8AnEXEyPQzBzIabi5vZwLTW3CA9OHRFRPRWi1hEHMTfkjSrlZ8KYGZmjeM1NzMzaxwXNzMzaxwXNzMzaxwXNzMzaxwXNzMzaxwXNzMzaxwXNzMza5w/Aduf3TwaVFKGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5a9da6a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#joinplot for first five item in data_corr_sorted\n",
    "i = 0\n",
    "for index, item in data_corr_sorted.iteritems():\n",
    "    feature_pair = index.split('_')\n",
    "    sns.jointplot(x=feature_pair[0], y=feature_pair[1], data=data, kind=\"kde\")\n",
    "    i = i + 1\n",
    "    if i > 5:\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分类\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对于部分值为0的数据，进行标记扩充\n",
    "feature_01_expand_list = ['Glucose', 'BloodPressure', 'Insulin', 'BMI']\n",
    "data_preprocessed = data.copy()\n",
    "for feature in feature_01_expand_list:\n",
    "    data_preprocessed[feature + '_01'] = data_preprocessed[feature].map(lambda x: 1 if x == 0 else 0 )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(data_preprocessed.drop(['Outcome'], axis = 1), data['Outcome'], test_size = 0.2, random_state = 4)\n",
    "\n",
    "mms_X = MinMaxScaler()\n",
    "X_train = mms_X.fit_transform(X_train)\n",
    "X_test = mms_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 分类\n",
    "\n",
    "> 默认参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "from sklearn.svm import LinearSVC,SVC\n",
    "from sklearn import metrics\n",
    "\n",
    "logistic = linear_model.LogisticRegression()\n",
    "\n",
    "logistic.fit(X_train, Y_train)\n",
    "Y_train_pred = logistic.predict(X_train)\n",
    "Y_test_pred = logistic.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default score for model on train set 0.762214983713355\n",
      "default score for model on test set 0.7922077922077922\n",
      "cross_val_score score for model on train set [0.50672441 0.5172518  0.4904863  0.45305292 0.48864748] mean 0.4912325820225532\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.81      0.89      0.85       102\n",
      "          1       0.74      0.60      0.66        52\n",
      "\n",
      "avg / total       0.79      0.79      0.79       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('default score for model on train set', logistic.score(X_train, Y_train))\n",
    "print('default score for model on test set', logistic.score(X_test, Y_test))\n",
    "loss = cross_val_score(logistic, data_preprocessed.drop(['Outcome'], axis = 1), data['Outcome'], cv = 5, scoring='neg_log_loss')\n",
    "print('cross_val_score score for model on train set', -loss, 'mean', -loss.mean())\n",
    "print(metrics.classification_report(Y_test,Y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "penaltys = ['l1', 'l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C=Cs)\n",
    "lr_penalty = linear_model.LogisticRegression()\n",
    "grid = GridSearchCV(lr_penalty, tuned_parameters, cv = 5, scoring='neg_log_loss')\n",
    "grid.fit(X_train, Y_train)\n",
    "Y_train_pred1 = grid.predict(X_train)\n",
    "Y_test_pred1 = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00079865, 0.00101118, 0.00040116, 0.00060086, 0.00100608,\n",
       "        0.0012044 , 0.00602756, 0.00080209, 0.00773959, 0.00129848,\n",
       "        0.00872335, 0.00130286, 0.00802274, 0.00120282]),\n",
       " 'mean_score_time': array([0.00061078, 0.00060239, 0.00079718, 0.00059552, 0.00081229,\n",
       "        0.00050621, 0.0007844 , 0.00060587, 0.00088835, 0.00050735,\n",
       "        0.00049968, 0.0006021 , 0.00060029, 0.00060201]),\n",
       " 'mean_test_score': array([-0.69314718, -0.67947613, -0.69314718, -0.65504639, -0.6464141 ,\n",
       "        -0.60591569, -0.4992885 , -0.51958847, -0.49108611, -0.49184669,\n",
       "        -0.49969295, -0.49698007, -0.50734269, -0.50283442]),\n",
       " 'mean_train_score': array([-0.69314718, -0.67933296, -0.69314718, -0.6538829 , -0.64570026,\n",
       "        -0.6001379 , -0.47499334, -0.50373037, -0.46063291, -0.4635745 ,\n",
       "        -0.46040518, -0.46046073, -0.46039933, -0.46040107]),\n",
       " 'param_C': masked_array(data=[0.001, 0.001, 0.01, 0.01, 0.1, 0.1, 1, 1, 10, 10, 100,\n",
       "                    100, 1000, 1000],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_penalty': masked_array(data=['l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1',\n",
       "                    'l2', 'l1', 'l2', 'l1', 'l2'],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 0.001, 'penalty': 'l1'},\n",
       "  {'C': 0.001, 'penalty': 'l2'},\n",
       "  {'C': 0.01, 'penalty': 'l1'},\n",
       "  {'C': 0.01, 'penalty': 'l2'},\n",
       "  {'C': 0.1, 'penalty': 'l1'},\n",
       "  {'C': 0.1, 'penalty': 'l2'},\n",
       "  {'C': 1, 'penalty': 'l1'},\n",
       "  {'C': 1, 'penalty': 'l2'},\n",
       "  {'C': 10, 'penalty': 'l1'},\n",
       "  {'C': 10, 'penalty': 'l2'},\n",
       "  {'C': 100, 'penalty': 'l1'},\n",
       "  {'C': 100, 'penalty': 'l2'},\n",
       "  {'C': 1000, 'penalty': 'l1'},\n",
       "  {'C': 1000, 'penalty': 'l2'}],\n",
       " 'rank_test_score': array([13, 12, 13, 11, 10,  9,  4,  8,  1,  2,  5,  3,  7,  6]),\n",
       " 'split0_test_score': array([-0.69314718, -0.67934777, -0.69314718, -0.65454064, -0.6509838 ,\n",
       "        -0.59852046, -0.45165642, -0.4875705 , -0.43804471, -0.44179448,\n",
       "        -0.43984819, -0.43978531, -0.44009099, -0.44008707]),\n",
       " 'split0_train_score': array([-0.69314718, -0.67949   , -0.69314718, -0.65469427, -0.64897502,\n",
       "        -0.60574913, -0.48837038, -0.51596884, -0.47442488, -0.47726609,\n",
       "        -0.47418041, -0.47423154, -0.47417768, -0.47417827]),\n",
       " 'split1_test_score': array([-0.69314718, -0.67982708, -0.69314718, -0.6565421 , -0.64788921,\n",
       "        -0.60884378, -0.46165184, -0.50653628, -0.43829133, -0.4477878 ,\n",
       "        -0.43661157, -0.43762433, -0.43645484, -0.43655394]),\n",
       " 'split1_train_score': array([-0.69314718, -0.67916387, -0.69314718, -0.65344612, -0.64891748,\n",
       "        -0.60066341, -0.48692125, -0.5120552 , -0.47412784, -0.47653795,\n",
       "        -0.47396284, -0.47400315, -0.47396102, -0.47396144]),\n",
       " 'split2_test_score': array([-0.69314718, -0.67880839, -0.69314718, -0.65287177, -0.64200288,\n",
       "        -0.60539406, -0.52357177, -0.53127802, -0.5069513 , -0.5058521 ,\n",
       "        -0.50840616, -0.50773414, -0.50857849, -0.50849809]),\n",
       " 'split2_train_score': array([-0.69314718, -0.67966118, -0.69314718, -0.65432172, -0.64158917,\n",
       "        -0.59835685, -0.46982557, -0.49803361, -0.45394338, -0.45715443,\n",
       "        -0.45372455, -0.45378311, -0.45372217, -0.45372281]),\n",
       " 'split3_test_score': array([-0.69314718, -0.6797482 , -0.69314718, -0.65587337, -0.64513518,\n",
       "        -0.60847025, -0.53788766, -0.53428225, -0.52598981, -0.51983325,\n",
       "        -0.52816268, -0.52688279, -0.52846425, -0.52833644]),\n",
       " 'split3_train_score': array([-0.69314718, -0.67915448, -0.69314718, -0.65347741, -0.64394149,\n",
       "        -0.59825258, -0.46715992, -0.49663377, -0.44947233, -0.45293084,\n",
       "        -0.44928141, -0.44934269, -0.44927889, -0.44927955]),\n",
       " 'split4_test_score': array([-0.69314718, -0.6796539 , -0.69314718, -0.6554179 , -0.64600858,\n",
       "        -0.60845138, -0.52256511, -0.53881133, -0.54732565, -0.54503269,\n",
       "        -0.58686284, -0.57420977, -0.62479829, -0.60222206]),\n",
       " 'split4_train_score': array([-0.69314718, -0.67919527, -0.69314718, -0.65347501, -0.64507814,\n",
       "        -0.59766753, -0.46268956, -0.49596042, -0.45119611, -0.45398318,\n",
       "        -0.4508767 , -0.45094317, -0.4508569 , -0.4508633 ]),\n",
       " 'std_fit_time': array([3.99456434e-04, 6.46212584e-04, 8.02326202e-04, 4.90602651e-04,\n",
       "        6.43575021e-04, 2.38826431e-04, 1.03514307e-05, 4.01047559e-04,\n",
       "        6.12135500e-04, 2.41310623e-04, 5.11493456e-04, 2.45593153e-04,\n",
       "        6.32722651e-04, 2.45378984e-04]),\n",
       " 'std_score_time': array([4.98776714e-04, 4.91848124e-04, 3.98824974e-04, 4.86353855e-04,\n",
       "        2.39543644e-04, 3.12933205e-04, 3.92320536e-04, 4.94789162e-04,\n",
       "        1.94341476e-04, 1.26492235e-05, 4.75500140e-06, 2.00581572e-04,\n",
       "        2.01526717e-04, 2.00510167e-04]),\n",
       " 'std_test_score': array([0.        , 0.0003719 , 0.        , 0.00126904, 0.00298478,\n",
       "        0.00392314, 0.03554971, 0.01961777, 0.04524917, 0.04064232,\n",
       "        0.05662598, 0.05246295, 0.06892162, 0.06142924]),\n",
       " 'std_train_score': array([0.        , 0.00020581, 0.        , 0.00052392, 0.00287944,\n",
       "        0.00298635, 0.01058935, 0.00851199, 0.01123114, 0.01097278,\n",
       "        0.01124925, 0.01124115, 0.01125228, 0.01125123])}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4910861145944954\n",
      "{'C': 10, 'penalty': 'l1'}\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.88      0.86       102\n",
      "          1       0.74      0.67      0.71        52\n",
      "\n",
      "avg / total       0.81      0.81      0.81       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)\n",
    "print(metrics.classification_report(Y_test,Y_test_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5ab1075c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    plt.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    plt.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'neg-logloss' )\n",
    "plt.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">线性SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'SVC1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-29-b979d49d8d32>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m print(\"Classification report for classifier %s:\\n%s\\n\"\n\u001b[1;32m----> 8\u001b[1;33m       % (SVC1, metrics.classification_report(Y_test, Y_test_pred2)))\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Confusion matrix:\\n%s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfusion_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_test_pred2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'SVC1' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "SVC_default = LinearSVC().fit(X_train, Y_train)\n",
    "#在校验集上测试，估计模型性能\n",
    "Y_test_pred2 = SVC_default.predict(X_test)\n",
    "\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (SVC1, metrics.classification_report(Y_test, Y_test_pred2)))\n",
    "print(\"Confusion matrix:\\n%s\" % metrics.confusion_matrix(Y_test, Y_test_pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> linear svm 正则调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for classifier LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.88      0.86       102\n",
      "          1       0.74      0.67      0.71        52\n",
      "\n",
      "avg / total       0.81      0.81      0.81       154\n",
      "\n",
      "\n",
      "Confusion matrix:\n",
      "[[90 12]\n",
      " [17 35]]\n"
     ]
    }
   ],
   "source": [
    "parameters = {'C':list(np.logspace(-3, 3, 7))}\n",
    "svc_grid = LinearSVC()\n",
    "grid_liner_svc = GridSearchCV(svc_grid, parameters, cv = 5)\n",
    "grid_liner_svc.fit(X_train, Y_train)\n",
    "#在校验集上测试，估计模型性能\n",
    "Y_test_pred3 = grid_liner_svc.predict(X_test)\n",
    "\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (svc_grid, metrics.classification_report(Y_test, Y_test_pred3)))\n",
    "print(\"Confusion matrix:\\n%s\" % metrics.confusion_matrix(Y_test, Y_test_pred3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 与lgoistic回归比较，在优化后结果相同，说明"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00080194, 0.00091057, 0.00100889, 0.0038177 , 0.02376642,\n",
       "        0.02517638, 0.02566719]),\n",
       " 'mean_score_time': array([0.00040641, 0.00050263, 0.00060253, 0.00019383, 0.00030222,\n",
       "        0.0004952 , 0.0003037 ]),\n",
       " 'mean_test_score': array([0.64820847, 0.65472313, 0.74104235, 0.75732899, 0.76221498,\n",
       "        0.75895765, 0.72149837]),\n",
       " 'mean_train_score': array([0.64820865, 0.65716002, 0.75690102, 0.7679015 , 0.7711585 ,\n",
       "        0.7650593 , 0.74223036]),\n",
       " 'param_C': masked_array(data=[0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0],\n",
       "              mask=[False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 0.001},\n",
       "  {'C': 0.01},\n",
       "  {'C': 0.1},\n",
       "  {'C': 1.0},\n",
       "  {'C': 10.0},\n",
       "  {'C': 100.0},\n",
       "  {'C': 1000.0}],\n",
       " 'rank_test_score': array([7, 6, 4, 3, 1, 2, 5]),\n",
       " 'split0_test_score': array([0.64516129, 0.65322581, 0.82258065, 0.81451613, 0.80645161,\n",
       "        0.7983871 , 0.66935484]),\n",
       " 'split0_train_score': array([0.64897959, 0.65102041, 0.73469388, 0.75102041, 0.75306122,\n",
       "        0.76326531, 0.67959184]),\n",
       " 'split1_test_score': array([0.6504065 , 0.65853659, 0.74796748, 0.78861789, 0.7804878 ,\n",
       "        0.7804878 , 0.75609756]),\n",
       " 'split1_train_score': array([0.64765784, 0.65580448, 0.73319756, 0.76374745, 0.76578411,\n",
       "        0.74338086, 0.77189409]),\n",
       " 'split2_test_score': array([0.6504065 , 0.65853659, 0.70731707, 0.76422764, 0.78861789,\n",
       "        0.78861789, 0.75609756]),\n",
       " 'split2_train_score': array([0.64765784, 0.65580448, 0.77393075, 0.77189409, 0.77800407,\n",
       "        0.77596741, 0.75560081]),\n",
       " 'split3_test_score': array([0.64754098, 0.6557377 , 0.69672131, 0.69672131, 0.70491803,\n",
       "        0.68852459, 0.70491803]),\n",
       " 'split3_train_score': array([0.64837398, 0.66056911, 0.77642276, 0.78455285, 0.78861789,\n",
       "        0.75813008, 0.75813008]),\n",
       " 'split4_test_score': array([0.64754098, 0.64754098, 0.7295082 , 0.72131148, 0.7295082 ,\n",
       "        0.73770492, 0.72131148]),\n",
       " 'split4_train_score': array([0.64837398, 0.66260163, 0.76626016, 0.76829268, 0.7703252 ,\n",
       "        0.78455285, 0.74593496]),\n",
       " 'std_fit_time': array([2.45885213e-04, 2.04986155e-04, 1.28007466e-05, 7.55459757e-04,\n",
       "        1.44486903e-03, 1.98594153e-04, 1.16447342e-03]),\n",
       " 'std_score_time': array([2.03296971e-04, 4.49847311e-04, 4.91965502e-04, 3.87668610e-04,\n",
       "        2.46764616e-04, 9.94865624e-06, 2.48360688e-04]),\n",
       " 'std_test_score': array([0.00199699, 0.00409062, 0.04468662, 0.04305521, 0.03829624,\n",
       "        0.04072486, 0.03291436]),\n",
       " 'std_train_score': array([0.00050116, 0.00406455, 0.01904581, 0.01091411, 0.01190615,\n",
       "        0.01428407, 0.03240005])}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_liner_svc.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.762214983713355\n",
      "{'C': 10.0}\n"
     ]
    }
   ],
   "source": [
    "print(-grid_liner_svc.best_score_)\n",
    "print(grid_liner_svc.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> rbf核svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters set found on development set:\n",
      "\n",
      "{'C': 1000, 'gamma': 0.005994842503189409, 'kernel': 'rbf'}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.648 (+/-0.004) for {'C': 1000, 'gamma': 1e-05, 'kernel': 'rbf'}\n",
      "0.648 (+/-0.004) for {'C': 1000, 'gamma': 3.5938136638046256e-05, 'kernel': 'rbf'}\n",
      "0.748 (+/-0.068) for {'C': 1000, 'gamma': 0.0001291549665014884, 'kernel': 'rbf'}\n",
      "0.756 (+/-0.096) for {'C': 1000, 'gamma': 0.0004641588833612782, 'kernel': 'rbf'}\n",
      "0.761 (+/-0.070) for {'C': 1000, 'gamma': 0.0016681005372000592, 'kernel': 'rbf'}\n",
      "0.774 (+/-0.068) for {'C': 1000, 'gamma': 0.005994842503189409, 'kernel': 'rbf'}\n",
      "0.774 (+/-0.067) for {'C': 1000, 'gamma': 0.021544346900318846, 'kernel': 'rbf'}\n",
      "0.764 (+/-0.040) for {'C': 1000, 'gamma': 0.07742636826811278, 'kernel': 'rbf'}\n",
      "0.756 (+/-0.058) for {'C': 1000, 'gamma': 0.2782559402207126, 'kernel': 'rbf'}\n",
      "0.726 (+/-0.056) for {'C': 1000, 'gamma': 1.0, 'kernel': 'rbf'}\n",
      "0.730 (+/-0.038) for {'C': 10000, 'gamma': 1e-05, 'kernel': 'rbf'}\n",
      "0.759 (+/-0.094) for {'C': 10000, 'gamma': 3.5938136638046256e-05, 'kernel': 'rbf'}\n",
      "0.759 (+/-0.084) for {'C': 10000, 'gamma': 0.0001291549665014884, 'kernel': 'rbf'}\n",
      "0.774 (+/-0.073) for {'C': 10000, 'gamma': 0.0004641588833612782, 'kernel': 'rbf'}\n",
      "0.774 (+/-0.076) for {'C': 10000, 'gamma': 0.0016681005372000592, 'kernel': 'rbf'}\n",
      "0.769 (+/-0.069) for {'C': 10000, 'gamma': 0.005994842503189409, 'kernel': 'rbf'}\n",
      "0.762 (+/-0.043) for {'C': 10000, 'gamma': 0.021544346900318846, 'kernel': 'rbf'}\n",
      "0.759 (+/-0.052) for {'C': 10000, 'gamma': 0.07742636826811278, 'kernel': 'rbf'}\n",
      "0.731 (+/-0.096) for {'C': 10000, 'gamma': 0.2782559402207126, 'kernel': 'rbf'}\n",
      "0.679 (+/-0.033) for {'C': 10000, 'gamma': 1.0, 'kernel': 'rbf'}\n",
      "0.756 (+/-0.091) for {'C': 100000, 'gamma': 1e-05, 'kernel': 'rbf'}\n",
      "0.770 (+/-0.070) for {'C': 100000, 'gamma': 3.5938136638046256e-05, 'kernel': 'rbf'}\n",
      "0.770 (+/-0.069) for {'C': 100000, 'gamma': 0.0001291549665014884, 'kernel': 'rbf'}\n",
      "0.774 (+/-0.073) for {'C': 100000, 'gamma': 0.0004641588833612782, 'kernel': 'rbf'}\n",
      "0.767 (+/-0.074) for {'C': 100000, 'gamma': 0.0016681005372000592, 'kernel': 'rbf'}\n",
      "0.756 (+/-0.045) for {'C': 100000, 'gamma': 0.005994842503189409, 'kernel': 'rbf'}\n",
      "0.756 (+/-0.058) for {'C': 100000, 'gamma': 0.021544346900318846, 'kernel': 'rbf'}\n",
      "0.739 (+/-0.066) for {'C': 100000, 'gamma': 0.07742636826811278, 'kernel': 'rbf'}\n",
      "0.721 (+/-0.059) for {'C': 100000, 'gamma': 0.2782559402207126, 'kernel': 'rbf'}\n",
      "0.664 (+/-0.040) for {'C': 100000, 'gamma': 1.0, 'kernel': 'rbf'}\n",
      "\n",
      "Detailed classification report:\n",
      "\n",
      "The model is trained on the full development set.\n",
      "The scores are computed on the full evaluation set.\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.87      0.86       102\n",
      "          1       0.73      0.67      0.70        52\n",
      "\n",
      "avg / total       0.80      0.81      0.80       154\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Set the parameters by cross-validation\n",
    "tuned_parameters = {'kernel': ['rbf'], 'gamma': list(np.logspace(-5, 0, 10)),\n",
    "                     'C': [ 1000,10000,100000]}\n",
    "\n",
    "clf = GridSearchCV(SVC(), tuned_parameters, cv=5)\n",
    "clf.fit(X_train, Y_train)\n",
    "\n",
    "print(\"Best parameters set found on development set:\")\n",
    "print()\n",
    "print(clf.best_params_)\n",
    "print()\n",
    "print(\"Grid scores on development set:\")\n",
    "print()\n",
    "means = clf.cv_results_['mean_test_score']\n",
    "stds = clf.cv_results_['std_test_score']\n",
    "for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
    "    print(\"%0.3f (+/-%0.03f) for %r\"\n",
    "              % (mean, std * 2, params))\n",
    "print()\n",
    "\n",
    "print(\"Detailed classification report:\")\n",
    "print()\n",
    "print(\"The model is trained on the full development set.\")\n",
    "print(\"The scores are computed on the full evaluation set.\")\n",
    "print()\n",
    "y_true, y_pred = Y_test, clf.predict(X_test)\n",
    "print(metrics.classification_report(y_true, y_pred))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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